Constraints

, Volume 13, Issue 1–2, pp 155–179

Boosting Haplotype Inference with Local Search

  • Inês Lynce
  • João Marques-Silva
  • Steve Prestwich
Article

Abstract

A very challenging problem in the genetics domain is to infer haplotypes from genotypes. This process is expected to identify genes affecting health, disease and response to drugs. One of the approaches to haplotype inference aims to minimise the number of different haplotypes used, and is known as haplotype inference by pure parsimony (HIPP). The HIPP problem is computationally difficult, being NP-hard. Recently, a SAT-based method (SHIPs) has been proposed to solve the HIPP problem. This method iteratively considers an increasing number of haplotypes, starting from an initial lower bound. Hence, one important aspect of SHIPs is the lower bounding procedure, which reduces the number of iterations of the basic algorithm, and also indirectly simplifies the resulting SAT model. This paper describes the use of local search to improve existing lower bounding procedures. The new lower bounding procedure is guaranteed to be as tight as the existing procedures. In practice the new procedure is in most cases considerably tighter, allowing significant improvement of performance on challenging problem instances.

Keywords

Haplotype inference Boolean satisfiability Local search 

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Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Inês Lynce
    • 1
  • João Marques-Silva
    • 2
  • Steve Prestwich
    • 3
  1. 1.IST/INESC-ID, Technical University of LisbonLisbonPortugal
  2. 2.School of Electronics and Computer ScienceUniversity of SouthamptonSouthamptonUK
  3. 3.Cork Constraint Computation CentreUniversity College CorkCorkIreland

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